{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_corr_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Fitting and training\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       1.00      0.33      0.50         9\n",
      "         Buy       0.38      0.83      0.53         6\n",
      "        Hold       0.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.47        17\n",
      "   macro avg       0.46      0.39      0.34        17\n",
      "weighted avg       0.67      0.47      0.45        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'penalty': ['l1', 'l2'],\n",
    "          'C': [1.5**n for n in range(0, 10, 2)],\n",
    "          'fit_intercept': [True, False],\n",
    "          'intercept_scaling': [1, 10, 50],\n",
    "          'solver': ['liblinear', 'saga']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 120 candidates, totalling 360 fits\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.506, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.444, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.417, test=0.275), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.484, test=0.293), total=   0.1s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.444, test=0.312), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.417, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.506, test=0.257), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.487, test=0.370), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.506, test=0.257), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.487, test=0.370), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.506, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.444, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.415, test=0.275), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.484, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.444, test=0.312), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.417, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.506, test=0.257), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.487, test=0.370), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.506, test=0.257), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.487, test=0.370), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.506, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.444, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.415, test=0.275), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.484, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.444, test=0.312), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.417, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.506, test=0.257), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.487, test=0.370), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.506, test=0.257), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.487, test=0.370), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.484, test=0.269), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.556, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.484, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.536, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.468, test=0.339), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.457, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.513, test=0.339), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.440, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.484, test=0.269), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.556, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.484, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.536, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.468, test=0.339), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.457, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.513, test=0.339), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.440, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.484, test=0.269), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.556, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.484, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.536, test=0.351), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.468, test=0.339), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.457, test=0.351), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.513, test=0.339), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.440, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.504, test=0.327), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.433, test=0.214), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.506, test=0.293), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.415, test=0.370), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.487, test=0.271), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.504, test=0.327), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.433, test=0.263), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.506, test=0.293), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.415, test=0.370), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.504, test=0.327), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.433, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.506, test=0.293), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.415, test=0.370), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.506, test=0.269), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.512, test=0.325), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.506, test=0.235), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.512, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.506, test=0.269), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.462, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.506, test=0.269), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.512, test=0.325), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.506, test=0.235), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.512, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.506, test=0.269), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.462, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.506, test=0.269), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.512, test=0.325), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.506, test=0.235), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.512, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.506, test=0.269), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.462, test=0.299), total=   0.0s\n",
      "[CV] C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=2.25, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.545, test=0.327), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.495, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.539, test=0.225), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.523, test=0.277), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.545, test=0.327), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.539, test=0.199), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.523, test=0.277), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.545, test=0.327), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.495, test=0.322), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.539, test=0.199), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.523, test=0.277), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.487, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.504, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.515, test=0.325), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.506, test=0.235), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.504, test=0.263), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.491, test=0.325), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.504, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.515, test=0.325), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.506, test=0.235), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.504, test=0.263), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.491, test=0.325), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.474, test=0.286), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.504, test=0.307), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.515, test=0.325), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.506, test=0.235), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.504, test=0.263), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.491, test=0.325), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=5.0625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.543, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.541, test=0.199), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.504, test=0.271), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.525, test=0.293), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.539, test=0.277), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.487, test=0.271), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.543, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.541, test=0.171), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.504, test=0.293), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.487, test=0.271), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.525, test=0.327), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.539, test=0.277), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.487, test=0.271), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.543, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.541, test=0.171), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.504, test=0.293), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.487, test=0.271), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.525, test=0.327), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.539, test=0.277), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.487, test=0.271), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.545, test=0.307), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.573, test=0.325), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.484, test=0.235), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.504, test=0.263), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.529, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.474, test=0.351), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.545, test=0.307), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.573, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.484, test=0.235), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.504, test=0.263), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.529, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.545, test=0.307), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.573, test=0.325), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.484, test=0.235), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.504, test=0.263), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.529, test=0.325), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.477, test=0.235), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=11.390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.562, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.541, test=0.171), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.504, test=0.271), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.544, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.539, test=0.248), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.504, test=0.271), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.581, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.558, test=0.171), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.504, test=0.271), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.544, test=0.325), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.539, test=0.248), total=   0.1s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.504, test=0.277), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.581, test=0.292), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.558, test=0.171), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.478, test=0.284), total=   0.1s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.504, test=0.271), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.544, test=0.325), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.539, test=0.248), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.504, test=0.257), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.478, test=0.284), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.487, test=0.271), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.543, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.618, test=0.298), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.454, test=0.235), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.504, test=0.269), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.529, test=0.325), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.454, test=0.235), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.543, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.618, test=0.298), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.454, test=0.235), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.504, test=0.269), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.529, test=0.325), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.454, test=0.235), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.543, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.618, test=0.298), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.454, test=0.235), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.468, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.504, test=0.269), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.529, test=0.325), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.454, test=0.235), total=   0.1s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.467, test=0.299), total=   0.0s\n",
      "[CV] C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=25.62890625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.491, test=0.351), total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done 360 out of 360 | elapsed:    5.7s finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='warn',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='warn',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': [1.0, 2.25, 5.0625, 11.390625, 25.62890625],\n",
       "                         'fit_intercept': [True, False],\n",
       "                         'intercept_scaling': [1, 10, 50],\n",
       "                         'penalty': ['l1', 'l2'],\n",
       "                         'solver': ['liblinear', 'saga']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.4927243359818397\n",
      "Mean Testing Score: 0.39677177177177175\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 2.25,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'penalty': 'l1',\n",
       " 'solver': 'saga'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2.25, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l1',\n",
       "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.75      0.33      0.46         9\n",
      "         Buy       0.33      0.67      0.44         6\n",
      "        Hold       0.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.41        17\n",
      "   macro avg       0.36      0.33      0.30        17\n",
      "weighted avg       0.51      0.41      0.40        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
